import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

# 读取数据
df = pd.read_excel('./associationRules/餐厅数据.xlsx')

# 转换菜品数据格式
trsations = df['菜品'].str.split(',').to_list()
ts = TransactionEncoder()
te_ary = ts.fit(trsations).transform(trsations)
df_encoded = pd.DataFrame(te_ary, columns=ts.columns_)

# print(df_encoded.head)

# 使用apriori进行关联规则挖掘
frequent_itemsets = apriori(df_encoded, min_support=0.1, use_colnames=True)
frequent_itemsets.sort_values(by='support', ascending=False, inplace=True)
# 选择2项集（注释状态，如需使用可取消注释）
# print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x : len(x) == 2)])
# 生成关联规则
rules = association_rules(frequent_itemsets, metric='confidence', min_threshold=0.1)
# 筛选有效规则
effective = rules[
    (rules['lift'] > 1) & (rules['confidence'] > 0.1)
].sort_values(by=['lift', 'confidence'], ascending=False)
# 生成相应的模型并保存
frequent_itemsets.to_pickle("frequent_itemsets.pkl")
effective.to_pickle("rule.pkl")